Guaranteeing High Availability Goals for Virtual Machine Placement

Bin, Eyal; Biran, Ofer; Boni, Odellia; Hadad, Erez; Kolodner, Eliot K.; Moatti, Yosef; Lorenz, Dean

doi:10.1109/icdcs.2011.72

Cited by 105 publications

(54 citation statements)

References 10 publications

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“…Significant reduction of downtime during migration is achieved by re-configurable distributed virtual machines (RDVM) [59], [78]. Multiple server failures are addressed by high available VM placement (HA-VM-Place) [62] and VM shadow-based placement solution (VM-SBS-Place) [63]. Another important aspect in the resilient VM placement problem relates to clustering.…”

Section: Techniques and Approachesmentioning

confidence: 99%

A Survey on Resiliency Techniques in Cloud Computing Infrastructures and Applications

Colman-Meixner

Develder

Tornatore

et al. 2016

IEEE Commun. Surv. Tutorials

127

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Abstract-Today's businesses increasingly rely on cloud computing, which brings both great opportunities and challenges. One of the critical challenges is resiliency: disruptions due to failures (either accidental or because of disasters or attacks) may entail significant revenue losses (e.g., US$ 25.5 billion in 2010 for North America). Such failures may originate at any of the major components in a cloud architecture (and propagate to others): (i) the servers hosting the application, (ii) the network interconnecting them (on different scales, inside a data center, up to wide-area connections), or (iii) the application itself. We comprehensively survey a large body of work focusing on resilience of cloud computing, in each (or a combination) of the server, network, and application components.First, we present the cloud computing architecture and its key concepts. We highlight both the infrastructure (servers, network) and application components. A key concept is virtualization of infrastructure (i.e., partitioning into logically separate units), and thus we detail the components in both physical and virtual layers. Before moving to the detailed resilience aspects, we provide a qualitative overview of the types of failures that may occur (from the perspective of the layered cloud architecture), and their consequences.The second major part of the paper introduces and categorizes a large number of techniques for cloud computing infrastructure resiliency. This ranges from designing and operating the facilities, servers, networks, to their integration and virtualization (e.g., also including resilience of the middleware infrastructure).The third part focuses on resilience in application design and development. We study how applications are designed, installed, and replicated to survive multiple physical failure scenarios as well as disaster failures.

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Section: Techniques and Approachesmentioning

confidence: 99%

A Survey on Resiliency Techniques in Cloud Computing Infrastructures and Applications

Colman-Meixner

Develder

Tornatore

et al. 2016

IEEE Commun. Surv. Tutorials

127

View full text Add to dashboard Cite

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“…Much work has been done regarding VM placement in the cloud computing environment, which is a complicated task involving various constraints, including performance [19], availability [20], network [21], and cost [22]. Economic interests are one popular topic that shows up in research literature [23,24].…”

Section: One-hop and Two-hop Comparisonmentioning

confidence: 99%

Migration-based virtual machine placement in cloud systems

Zheng

2013

2013 IEEE 2nd International Conference on Cloud Networking (CloudNet)

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Abstract-Cloud computing is an emerging technology that greatly shapes our lives, where users run their jobs on virtual machines (VMs) on physical machines (PMs) provided by a cloud service provider, saving the investment in upfront infrastructures. Due to the heterogeneity of various jobs, different VMs on the same PMs could have different job completion times. Meanwhile, the PMs are also heterogeneous. Therefore, different VM placements have different job completion times, and our objective is to minimize the total job completion time of the input VM requests through a reasonable VM placement schedule. This problem is NP-hard, since it can be reduced to a knapsack problem. We propose an off-line VM placement method through an emulated VM migration process, while the on-line VM placement is solved by a real VM migration process. The migration algorithm is a heuristic approach, in which we place the VM to its best PM directly, if this PM has enough capacity. Otherwise, we migrate another VM from this PM to accommodate the new VM, if a prespecified migration constraint is satisfied. Furthermore, we study a hybrid scheme where a batch is employed to accept upcoming VMs for the on-line scenario. Evaluation results validate the high efficiency of the proposed algorithms.

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“…Constraint programming based approaches have also been used previously to solve some related problems [6,3,8]. In [3] a data centre is viewed as a dynamic bin packing system where servers host applications with varying resource requirements and varying relative placement constraints.…”

Section: Related Workmentioning

confidence: 99%

“…In [3] a data centre is viewed as a dynamic bin packing system where servers host applications with varying resource requirements and varying relative placement constraints. However, their work can be seen as a reactive approach where the servers are reconfigured when the current configuration is no longer viable.…”

Section: Related Workmentioning

confidence: 99%

“…For example, a report by the EU Stand-by Initiative stated that in 2007 Western European data centres consumed 56 Tera-Watt Hours (TWh) of power, which is expected to almost double to 104 TWh per year by 2020. 3 Nevertheless, as reported by the consulting firm McKinsey, only 6-12% of electricity used by data centres can be attributed to the performance of productive computation [7]. Therefore, one of the optimisation challenges in the domain of data centres is to keep servers well utilised so that energy costs can be reduced.…”

Section: Introductionmentioning

confidence: 99%

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Constraint Programming Based Large Neighbourhood Search for Energy Minimisation in Data Centres

Cambazard

Mehta

O’Sullivan

et al. 2013

Economics of Grids, Clouds, Systems, and Services

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Abstract. EnergeTIC is a recent industrial research project carried out in Grenoble on optimising energy consumption in data centres. We study the problem formulation proposed by EnergeTIC. The problem focuses on the allocation of virtual machines to servers with time-variable resource demands in data centres in order to minimise energy costs while ensuring service quality. We present a scalable constraint programming-based large neighbourhood search (CP-LNS) method to solving this challenging problem. We present empirical results that demonstrate that the industrial benchmarks can be solved to near optimality using our approach. Our CP-LNS method provides a fast and practical approach for finding high quality solutions for lowering electricity costs in data centres.

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Guaranteeing High Availability Goals for Virtual Machine Placement

Cited by 105 publications

References 10 publications

A Survey on Resiliency Techniques in Cloud Computing Infrastructures and Applications

A Survey on Resiliency Techniques in Cloud Computing Infrastructures and Applications

Migration-based virtual machine placement in cloud systems

Constraint Programming Based Large Neighbourhood Search for Energy Minimisation in Data Centres

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